##plugins.themes.bootstrap3.article.main##

The exploration of Hadith sciences gains significant consideration over the most recent couple of years. Hadith is mostly the sayings of Prophet Mohammad. The Holy Quran represents the first origin of law in Islam then Hadith takes the second role. Many research efforts manage Hadith with respect to the “Isnad” and “Matn”; which are the main two pieces of Hadith. In this paper, we examine the chance of utilizing Deep Learning to process Isnad of Hadiths. Consequently, a definitive objective of our framework is to help in the systematic classification of Hadiths and differentiate among the correct (“Sahih”) Hadiths and the not accurate (“Da'ief”) Hadiths.

Downloads

Download data is not yet available.

References

  1. A. Abdelkader, D. Souilem Boumiza and R. Braham, “A categorization algorithm for the Arabic language,” International Conference on Communication, Computer and Power (ICCCP'09), Muscat, February 2009.
     Google Scholar
  2. NLP4Arabic. Available online: https://sites.google.com/site/nlp4arabic/ (Accessed on 12 Jul 2020).
     Google Scholar
  3. A. Farghaly, K. Shaalan, “Arabic natural language processing: challenges and solutions”. ACM Trans. Asian Lang. Inform. Process. 8, 4, Article 14, 22 pages, December 2009.
     Google Scholar
  4. K. Shaalan, “Rule-based approach in Arabic natural language processing,” International Journal on Information and Communication Technologies, Vol. 3, No. 3, June 2010.
     Google Scholar
  5. M. Al-Hajjaj, “Sahih Muslim” [Muslim probe] (in Arabic), Dar Ibn Al-Jawzi Publication and Distributors, Egypt, 2009.
     Google Scholar
  6. M. Najeeb, A. Abdelkader, and M. Al-Zghoul, “Arabic natural language processing laboratory serving Islamic sciences,” Int. J. Adv. Comput. Sci. Applic. 2014, vol. 5, no. 3, pp. 114-117.
     Google Scholar
  7. M. Najeeb, “A Novel Hadith Processing Approach Based on Genetic Algorithms,” IEEE Access, vol. 8, 2020, pp. 20233-20244.
     Google Scholar
  8. M. Najeeb, “XML Database for Hadith and Narrators,” American Journal of Applied Sciences. 2016, vol. 13, no. 1, pp. 55-63.
     Google Scholar
  9. M. Najeeb, A. Abdelkader, M. Al-Zghoul, and A. Osman, “A lexicon for hadith science based on a corpus,” Int. J. Comput. Sci. Inform. Technol. 2015, vol. 6, no. 2 pp. 1336-1340.
     Google Scholar
  10. M. Saloot, N. Idris, R. Mahmud, S. Ja’afar, D. Thorleuchter, and A. Gani, “Hadith data mining and classification: a comparative analysis,” Artif Intell Rev. 2016, vol. 46, pp. 113 – 128.
     Google Scholar
  11. F. Harrag, “Text mining approach for knowledge extraction in Sahîh Al-Bukhari,” Comput Hum Behav. 2014, vol. 30, pp. 558–566.
     Google Scholar
  12. K. Aldhaln, A. Zeki, A. Zeki, and H. Alreshidi, “Improving knowledge extraction of Hadith classifier using decision tree algorithm,” Int. conf. on information retrieval & knowledge management, 2012, Malaysia, pp. 148–152.
     Google Scholar
  13. M. Alhawarat, “A domain-based approach to extract Arabic person names using n-grams and simple rules,” Asian Journal of Information Technology. 2015, vol. 14, no. 8, pp. 287–293.
     Google Scholar
  14. A. Mahmood, H. Khan, F. Alarfaj, M. Ramzan, and M. Ilyas, “A multilingual datasets repository of the hadith content,” International Journal of Advanced Computer Science and Applications. 2018, vol. 9, no. 2, pp. 165–172.
     Google Scholar
  15. Shamela.موقع المكتبة الشاملة على الشبكة العنكبوتية , Available online: http://shamela.ws (Accessed on 16 Jul 2020).
     Google Scholar
  16. Dorar. موقع الدرر السنية على الشبكة العنكبوتية, Available online: http://www.dorar.net (Accessed on 16 Jul 2020).
     Google Scholar
  17. Islamweb. موقع إسلام ويب على الشبكة العنكبوتية, Available online: http://www.islamweb.net (Accessed on 16 Jul 2020).
     Google Scholar
  18. Sonnaonline. الجامع للحديث النبوي, Available online: http://www.sonnaonline.com (Accessed on 20 Jul 2020).
     Google Scholar
  19. Sunnah. Sayings and teachings of Prophet Muhammad, Available online: https://sunnah.com (Accessed on 20 Jul 2020).
     Google Scholar
  20. Sunnah Alifta. جامع خادم الحرمين الشريفين للسنة النبوية, Available online: https://sunnah.alifta.gov.sa (Accessed on 20 Jul 2020).
     Google Scholar
  21. A. Azmi and N. Bin Badia, "iTree - Automating the construction of the narration tree of Hadiths (Prophetic Traditions)," Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering (NLPKE-2010), Beijing, China, 2010, pp. 1-7.
     Google Scholar
  22. F. Harrag, A. Hamdi-Cherif and E. El-Qawasmeh, "Vector space model for Arabic information retrieval — application to “Hadith” indexing," 2008 First International Conference on the Applications of Digital Information and Web Technologies (ICADIWT), Ostrava, Czech Republic, 2008, pp. 107-112.
     Google Scholar
  23. K. Aldhlan, A. Zeki, A. Zeki, “Data mining and Islamic Knowledge Extraction: Hadith as A Knowledge Resource”, Proceeding 3rd International Conference on ICT4M, 2010.
     Google Scholar
  24. H. Alrazo, “Al-'Utur al-ma'lumatiah le tadawel al-ma'refah aleslamiah fi zaman al-a'wlamh: Information frame works to deal with Islamic Knowledge in globalization era”. Journal of Islamic knowledge 4, pp. 33-34, 2003.
     Google Scholar
  25. H. Alrazo. “Data mining application on the Islamic knowledge resource”, from Alukah: http://www.alukah.net/Culture/0/3123/ (Accessed on 17 Aug 2020).
     Google Scholar
  26. M. Ghazizadeh, M. Zahedi, M. Kahani, and B. Bidgoli, “Fuzzy Expert system in determining Hadith validity”, advances in computer and information sciences and engineering, PP.354-359, 2008.
     Google Scholar
  27. M. Hyder and S. Ghazanfer, “Towards a database Oriented Hadith Research Using Relational, Algorithmic and Data-warehousing Techniques”, The Islamic Culture, Quarterly Journal of Shaikh Zayed Islamic Center for Islamic and Arabic Studies, Vol. 19, University of Karachi, 2008.
     Google Scholar
  28. F. Harrag, E. El-Qawasmeh and A. Al-Salman, “Extracting Named Entities from Prophetic Narration Texts (Hadith)”, ICSECS 2011, Part II, CCIS 180, pp. 289–297, 2011.
     Google Scholar
  29. K. Bilal and S. Mohsin, “Muhadith: A Cloud based Distributed Expert System for Classification of Ahadith”, IEEE 10th International Conference on Frontiers of Information Technology, pp. 73-78, 2012.
     Google Scholar
  30. W. Sari, M. Arif Bijaksana, and A. Huda, “Indexing Name in Hadith Translation Using Hidden Markov Model (HMM),” 7th International Conference on Information and Communication Technology (ICoICT), 2019, Kuala Lumpur, Malaysia, pp. 1-5, 2019.
     Google Scholar
  31. I. Bounhas, “On the Usage of a Classical Arabic Corpus as a Language Resource: Related Research and Key Challenges,” ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP). 2019, vol. 18, no. 3, Art. no. 23.
     Google Scholar
  32. A. Azmi, A. Al-Qabbany, and A. Hussain, “Computational and natural language processing-based studies of hadith literature: a survey,” Artificial Intelligence Review. 2019, vol. 52, no. 2, pp. 1369-1414.
     Google Scholar
  33. S. Minaee, N. Kalchbrenner, E. Cambria, N. Nikzad, M. Chenaghlu, J. Gao Deep learning-based text classification: a comprehensive review, arXiv Preprint arXiv:2004.03705 (2020).
     Google Scholar
  34. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, 1998.
     Google Scholar
  35. Microscope uses artificial intelligence to find cancer cells more efficiently. Available online: https://cnsi.ucla.edu/blog/2016/04/13/microscope-uses-artificial-intelligence-to-find-cancer-cells-more-efficiently (Accessed on 18 Nov 2020).
     Google Scholar
  36. Deep Image Prior. Available online: https://dmitryulyanov.github.io/deep_image_prior (Accessed on 25 Nov 2020).
     Google Scholar
  37. How deep learning is changing the game for both advertisers and consumers. Available online: https://www.clickz.com/how-deep-learning-is-changing-the-game-for-both-advertisers-and-consumers/110486/ (Accessed on 25 Nov 2020).
     Google Scholar
  38. Machine Learning for Translation: What’s the State of the Language Art? Available online: https://readwrite.com/2019/11/02/machine-learning-for-translation-whats-the-state-of-the-language-art (Accessed on 29 Nov 2020).
     Google Scholar
  39. Why deep-learning AIs are so easy to fool. Available online: https://www.nature.com/articles/d41586-019-03013-5 (Accessed on 29 Nov 2020).
     Google Scholar
  40. R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts, “Recursive deep models for semantic compositionality over a sentiment treebank,” in Proceedings of the 2013 conference on empirical methods in natural language processing, 2013, pp. 1631–1642.
     Google Scholar
  41. Q. V. Le and T. Mikolov, “Distributed representations of sentences and documents,” in Proc. ICML, 2014, pp. 1188–1196, 2014.
     Google Scholar
  42. T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” in Proc. ICLR, 2013.
     Google Scholar
  43. P. Liu, X. Qiu, and X. Huang, “Recurrent neural network for text classification with multi-task learning,” arXiv preprint arXiv:1605.05101, 2016.
     Google Scholar
  44. Y. Kim, “Convolutional neural networks for sentence classification,” in EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, 2014.
     Google Scholar
  45. D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” in Proc. ICLR, 2015.
     Google Scholar
  46. Z. Yang, D. Yang, C. Dyer, X. He, A. J. Smola, and E. H. Hovy, “Hierarchical attention networks for document classification,” in Proc. NAACL, 2016, pp. 1480–1489, 2016.
     Google Scholar
  47. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems, 2017, pp. 5998–6008.
     Google Scholar
  48. Y. Li, R. Jin, and Y. Luo, “Classifying relations in clinical narratives using segment graph convolutional and recurrent neural networks (seggcrns),” JAMIA, vol. 26, no. 3, pp. 262–268, 2019.
     Google Scholar
  49. D. Marcheggiani and I. Titov, “Encoding sentences with graph convolutional networks for semantic role labeling,” in Proc. EMNLP, 2017, pp. 1506–1515, 2017.
     Google Scholar
  50. J. Bastings, I. Titov,W. Aziz, D. Marcheggiani, and K. Sima’an, “Graph convolutional encoders for syntax-aware neural machine translation,” in Proc. EMNLP, 2017, pp. 1957–1967, 2017.
     Google Scholar